가스센서 어레이와 패턴인식을 활용한 아로마 오일의 특성 분류
Characteristic Classification of Aroma Oil with Gas Sensors Array and Pattern Recognition
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초록
An evaluation system for an electronic-nose concept using three types of metal oxide gas sensors that react similarly to the human olfactory cells was constructed for the quantitative and qualitative evaluation of aroma fragrances. Four types of aroma fragrances (lav- ender, orange, jasmine, and Roman chamomile), which are commonly used in aromatherapy, were evaluated. All the gas sensors reactedremarkably to the aroma fragrances and the good correlation of r=0.58–0.88 with the aromatic odor intensities by olfaction was con-firmed. From the results of the analysis of an electronic-nose concept for classifying the characteristics of aroma oil fragrances, aromaoils could be classified using the fragrance characteristics and oil extraction methods with the cumulative variability contribution rate of 95.65% (F1: 69.65%, F2: 26.03%) by principal component analysis. In the pattern recognition based on the artificial neural network, the four aroma fragrances were 100% recognized through the training data of 56 cases (70%) out of 80 cases, and the pattern recognitionrate was 57.1%–71.4% through the validation and testing data of 24 cases (30%). The pattern recognition success rate through all con-fusion matrices was 82.1%, indicating that the classification of aroma oil fragrances using the three types of gas sensors was successful.
Abstract
An evaluation system for an electronic-nose concept using three types of metal oxide gas sensors that react similarly to the human olfactory cells was constructed for the quantitative and qualitative evaluation of aroma fragrances. Four types of aroma fragrances (lav- ender, orange, jasmine, and Roman chamomile), which are commonly used in aromatherapy, were evaluated. All the gas sensors reactedremarkably to the aroma fragrances and the good correlation of r=0.58–0.88 with the aromatic odor intensities by olfaction was con-firmed. From the results of the analysis of an electronic-nose concept for classifying the characteristics of aroma oil fragrances, aromaoils could be classified using the fragrance characteristics and oil extraction methods with the cumulative variability contribution rate of 95.65% (F1: 69.65%, F2: 26.03%) by principal component analysis. In the pattern recognition based on the artificial neural network, the four aroma fragrances were 100% recognized through the training data of 56 cases (70%) out of 80 cases, and the pattern recognitionrate was 57.1%–71.4% through the validation and testing data of 24 cases (30%). The pattern recognition success rate through all con-fusion matrices was 82.1%, indicating that the classification of aroma oil fragrances using the three types of gas sensors was successful.
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